neuromod 0.5.0

A high-performance Rust SNN library for neuroscience research and pure spiking neural network library featuring LIF, Izhikevich, Hebbian, Nagumo, Lapicque and Hodgkin-Huxley dynamics.
Documentation 
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//! Example demonstrating optional Sentry error reporting.
//!
//! Run with: cargo run --features sentry --example sentry
//!
//! Set SENTRY_DSN environment variable to enable real reporting.

fn main() {
    // Guarded initialization - only when the "sentry" feature is enabled.
    #[cfg(feature = "sentry")]
    let _guard = {
        let dsn = std::env::var("SENTRY_DSN").unwrap_or_default();
        if !dsn.is_empty() {
            // Validate DSN before initialization
            match dsn.parse::<sentry::types::Dsn>() {
                Ok(parsed_dsn) => {
                    let guard = sentry::init((
                        parsed_dsn,
                        sentry::ClientOptions {
                            release: sentry::release_name!(),
                            ..Default::default()
                        },
                    ));
                    println!("Sentry initialized for error monitoring (feature enabled)");
                    sentry::capture_message(
                        "Sentry integration active in neuromod example",
                        sentry::Level::Info,
                    );
                    Some(guard)
                }
                Err(e) => {
                    eprintln!("Invalid SENTRY_DSN format: {}", e);
                    println!("Sentry feature enabled but not reporting due to invalid DSN.");
                    None
                }
            }
        } else {
            println!("SENTRY_DSN not set; Sentry feature enabled but not reporting.");
            None
        }
    };

    #[cfg(not(feature = "sentry"))]
    {
        println!("Sentry feature not enabled. Running without error reporting.");
    }

    // Normal neuromod usage (always runs)
    use neuromod::{NeuroModulators, SpikingNetwork};
    let mut network = SpikingNetwork::new();
    let stimuli = [0.5f32; 16];
    let modulators = NeuroModulators::default();
    let _spikes = network.step(&stimuli, &modulators).expect("step failed");
    println!("neuromod step completed successfully.");
}